摘要
Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.
作者
王伟峰
YANG Ze
LI Zhao
ZHAO Xuanchong
WANG Weifeng;YANG Ze;LI Zhao;ZHAO Xuanchong(School of Safety Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,P.R.China)
基金
the National Key R&D Plan of China(No.2021YFE0105000)
the National Natural Science Foundation of China(No.52074213)
the Shaanxi Key R&D Plan Project(No.2021SF-472)
the Yulin Science and Technology Plan Project(No.CXY-2020-036).
